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Internship Graduate Machine Learning Jobs in Illinois

Prior internship, research, or applied ML project experience with measurable outcomes What You'll Gain * Ownership of real machine learning experiments with direct business visibility * Experience ...

Sr. Machine Learning Engineer

Schaumburg, IL · On-site

$103K - $141.40K/yr

I wont accept opts with internships Actalent/Peyton Hello Muni, We received a new role from Toyota. Max rate is 95/hr. Toyota Connected's Mobility team is looking for a Sr. Machine Learning Engineer ...

Senior Machine Learning Engineer

Chicago, IL · On-site +1

$107.60K - $147.80K/yr

Senior Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part ... At least 4 years of experience programming with Python, Scala, or Java (Internship experience does ...

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Internship Graduate Machine Learning information

What are the key skills and qualifications needed to thrive as an Internship Graduate in Machine Learning, and why are they important?

To thrive as an Internship Graduate in Machine Learning, you typically need a strong background in mathematics, programming (especially Python), and familiarity with algorithms and data structures, often supported by coursework or a degree in computer science, statistics, or a related field. Hands-on experience with machine learning frameworks like TensorFlow or PyTorch, and knowledge of tools such as Jupyter Notebooks and version control systems like Git, are highly valued. Curiosity, problem-solving, teamwork, and effective communication are crucial soft skills to excel in collaborative and innovative environments. These competencies enable interns to contribute to real-world projects, adapt to fast-changing technologies, and communicate findings clearly within interdisciplinary teams.

What types of projects do Internship Graduate Machine Learning roles typically involve, and how are responsibilities structured within the team?

Internship Graduate Machine Learning roles often focus on supporting ongoing research or development projects, such as building predictive models, cleaning and analyzing data, or prototyping algorithms. Interns usually collaborate closely with data scientists and engineers, contributing to specific project milestones while learning best practices in model development and deployment. Responsibilities are often structured to allow for mentorship and feedback, with interns participating in regular team meetings, code reviews, and brainstorming sessions. This collaborative environment provides valuable exposure to real-world machine learning workflows and helps interns build both technical and soft skills relevant to the field.

What are Internship Graduate Machine Learning positions?

Internship Graduate Machine Learning positions are entry-level roles designed for recent graduates or students who have completed coursework in machine learning, data science, or related fields. These internships provide hands-on experience working with real-world data, building and testing machine learning models, and collaborating with experienced professionals. Interns gain exposure to industry-standard tools and techniques, helping them bridge the gap between academic learning and practical application. Such positions are valuable for building a portfolio, networking, and enhancing job prospects in the rapidly growing field of artificial intelligence.

What is the difference between Internship Graduate Machine Learning vs Data Analyst?

AspectInternship Graduate Machine LearningData Analyst
Required CredentialsDegree in Computer Science, Data Science, or related field; basic knowledge of programming and statisticsDegree in Statistics, Mathematics, or related field; proficiency in data visualization and analysis tools
Work EnvironmentTech companies, research labs, startups; project-based, collaborative teamsBusiness, finance, marketing sectors; focus on reporting and data interpretation
Employer & Industry UsageUsed in tech, AI, and research industries for developing machine learning modelsCommon in corporate, finance, and consulting firms for data-driven decision making

While both roles involve working with data, an Internship Graduate Machine Learning focuses on developing algorithms and models using programming skills, often in tech environments. In contrast, a Data Analyst emphasizes interpreting data, creating reports, and supporting business decisions. The roles overlap in data handling but differ in technical depth and application focus.

What cities in Illinois are hiring for Internship Graduate Machine Learning jobs? Cities in Illinois with the most Internship Graduate Machine Learning job openings:
Machine Learning Co-Op

Machine Learning Co-Op

Kop-Coat, Inc.

Vernon Hills, IL • On-site

$28 - $30/hr

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

CoOp Student - Machine Learning & Applied AI

Location: Hybrid - Minimum 3 days per week onsite (Vernon Hills, IL)
Duration: CoOp Term (6-8 months)
Department: Automation & Emerging Technology
Reports To: Emerging Technologies Leader
Candidate Level: Bachelor's, Master's, or PhDtrack students


Position Overview

We are seeking a highly motivated Machine Learning & Applied AI CoOp Student to join our Automation & Emerging Technology team. This role is ideal for students who want handson ownership of realworld machine learning experiments in a fastmoving, startuplike environment within a large enterprise.

The coop will focus on applied machine learning, datadriven experimentation, and model evaluation, with opportunities to explore Generative AI and large language models where they meaningfully support MLdriven use cases. Rather than production maintenance or traditional automation work, this role emphasizes problem framing, experimentation, and measurable impact.

This position follows a hybrid work model, with a minimum of three (3) days per week onsite at our Vernon Hills, IL office.


Key Responsibilities

  • Lead machine learning experiments endtoend, including:
    • Problem definition and hypothesis development
    • Data exploration and feature engineering
    • Model prototyping, training, and evaluation
    • Iteration based on quantitative results
  • Develop and evaluate ML models using enterprise datasets for use cases such as:
    • Prediction and classification
    • Pattern detection and insight generation
    • Decision support and optimization
  • Apply sound experimental design and evaluation techniques, including:
    • Train/validation/test strategies
    • Baseline comparisons
    • Error analysis and model diagnostics
  • Use Databricks for data analysis, experimentation, and scalable ML workflows
  • Define and track success metrics, such as:
    • Model accuracy, precision/recall, and robustness
    • Latency, scalability, and cost considerations
    • Business relevance and usability
  • Explore applied AI techniques, including Generative AI and LLMs, where appropriate (e.g., summarization, knowledge retrieval, or hybrid ML + LLM solutions)
  • Document experiments, assumptions, results, and technical tradeoffs; present findings and demos to technical and business stakeholders
  • Apply Responsible AI and data governance practices, including data privacy, security, and bias awareness

Required Qualifications

  • Currently enrolled in a Bachelor's, Master's, or PhDtrack program in Computer Science, Data Science, Machine Learning, Statistics, or a related field
  • Ability to work onsite in Vernon Hills, IL at least three days per week
  • Strong proficiency in Python
  • Solid understanding of core machine learning concepts, such as:
    • Supervised and unsupervised learning
    • Feature engineering
    • Model evaluation and validation
  • Experience with common ML/data libraries (e.g., pandas, NumPy, scikitlearn, or similar)
  • Experience with AI Tools like Copilot, Copilot GitHub etc.
  • Ability to work independently, take initiative, and operate effectively in ambiguous problem spaces
  • Strong analytical thinking and communication skills

Preferred Qualifications

  • Handson experience with endtoend ML projects, including experimentation and evaluation
  • Familiarity with Databricks or similar data/ML platforms
  • Exposure to cloudbased ML workflows (Azure preferred)
  • Experience with deep learning or NLP frameworks (e.g., PyTorch, TensorFlow, Hugging Face)
  • Working knowledge of Generative AI or LLMs as an applied technique (not required)
  • Prior internship, research, or applied ML project experience with measurable outcomes

What You'll Gain

  • Ownership of real machine learning experiments with direct business visibility
  • Experience working in a startuplike, experimentdriven environment inside a large enterprise
  • Handson exposure to enterprisescale data and ML workflows using Databricks and Microsoft platforms
  • Mentorship from experienced AI and Emerging Technology leaders
  • Strong preparation for fulltime roles in Machine Learning Engineering, Applied Data Science, or AI Engineering

Salary Target Range: $28/hr-$30/hr 

Rust-Oleum is an equal opportunity employer. Employment selection and related decisions are made without regard to sex, race, age, disability, religion, national origin, color, or any other protected class.

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